Sensing Wettability Condition of Insulation Surface Employing Convolutional Neural Network

Chatterjee, Soumya; Roy, Sayanjit Singha; Samanta, Kaniska; Modak, Sudip

doi:10.1109/lsens.2020.3002991

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…To overcome this issue, numerous researchers have proposed digital image processing methods to analyze and quantify the hydrophobicity class. In [118], the spray method was used to generate a huge amount of data images which were fed to a deep convolutional neural network model (AlexNet) for the purpose of wettability classification. Compared to other machine learning algorithms, deep learning overcomes the manual dependency on feature extraction and involves less training time due to the transferred learning approach that was used in the article.…”

Section: Physical Defect Detectionmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

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Section: Physical Defect Detectionmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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“…It is a challenging task to acquire a certain number of insulators with HC1–HC7. Some researchers proposed that using different percentage of isopropyl alcohol by volume as spraying solution could artificially simulate different HCs [12–17]. Isopropyl alcohol has a lower surface tension than water, spray it on the insulator surface, the droplets formed consistent with weak hydrophobic.…”

Section: Spray Images Collectionmentioning

confidence: 99%

“…Convolutional neural network (CNN) is widely used in intelligent image recognition tasks due to its excellent feature extraction ability. In order to overcome the limitations of the subjective feature extraction, some researchers had applied CNN to HC recognition of composite insulators [16,17], and already verified the feasibility. However, the generalization ability of the CNN model under actual conditions, including various shooting angles and distances, different ambient lighting and surface conditions, is not considered at present, which limits the practical application of this technology.…”

Section: Introductionmentioning

confidence: 99%

Hydrophobicity Classification of Composite Insulators Based on Light‐Weight Convolutional Neural Networks

Qiu

Liu

Liao

et al. 2022

IEEJ Transactions Elec Engng

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To achieve accurate and rapid measurement of the hydrophobicity class (HC) of composite insulators, an intelligent spray image recognition technique based on light‐weight convolutional neural networks (CNN) is proposed in this paper. A spray image data set contains clean, contaminated and aged insulators with various illuminations, shooting angles and distances, about 10 400 images of shed surface were collected by spray tests and data augmentation. Five classification models were established by different CNNs, including GoogLeNet, ResNet101, ShuffleNet 0.5×, ShuffleNet 0.25× and MobileNet V2, while the first four of them were pre‐trained by ImageNet dataset. These models were trained, validated and tested by spray image data set. Six indexes were designed to evaluate each model and the discriminative regions for classification were visualized by gradient weighted class activation mapping (Grad‐CAM) method. The results show that these models can effectively recognize spray images with HC1–HC7 and the light‐weight ShuffleNet 0.5× has the best performance, with the classification accuracy of 97.09% for 2022 test images. The Grad‐CAM visualizations indicate that the results have high reliability. This study can provide reference for on‐line detection and intelligent identification of hydrophobicity levels of composite insulators. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…To improve the classification accuracy in any machine learning problem, the mode of feature extraction plays an important role [10]. Handcrafted feature extraction technique is not only a tedious process but also it may cause the generation of redundant features, which may lead to misclassification.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [10], deep learning models have been successfully applied for hydrophobicity class assessment of silicone rubber insulators. Motivated by these achievements, this article proposed a deep learning framework for contactless measurement of contamination severity of overhead insulator employing IRT image.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

Das

Deb

Chatterjee

et al. 2022

IET Signal Processing

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In this paper, a novel vision-based automated framework for estimation of pollution severity of outdoor insulators is proposed. The correct estimation of pollution severity is important to prevent the premature flashover of insulators. Existing methods to determine the degree of contamination of insulator surface requires direct contact with the insulator, which is practically problematic. Considering the aforesaid fact, in this article, a novel infrared thermal (IRT) image-based automated framework is proposed for noncontact monitoring the surface condition of outdoor insulators. A large number of IRT images corresponding to different contamination levels were captured from several porcelain disc insulators using a thermal camera. The captured IRT images were initially segmented using mask region-based convolutional neural network to remove the effect of background. Then, a hybrid deep learning network consisting of convolutional neural network and Bi-directional long short memory (CNN-BiLSTM) is designed for automated classification of IRT images. It has been observed that the proposed network has achieved an accuracy of 98.86%, specificity of 99.72%, precision of 98.86% and F-1 score of 98.86%, respectively. A comparative study with other deep learning models indicated that the proposed CNN-BiLSTM network delivered better performance. Hence, the proposed framework can be used in real-life for non-contact condition monitoring of insulators.

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Sensing Wettability Condition of Insulation Surface Employing Convolutional Neural Network

Cited by 19 publications

References 9 publications

Deep Learning in High Voltage Engineering: A Literature Review

Deep Learning in High Voltage Engineering: A Literature Review

Hydrophobicity Classification of Composite Insulators Based on Light‐Weight Convolutional Neural Networks

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

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